Data Lake vs Data Lakehouse: The Evolution of Data Storage

As organizations strive to extract meaningful insights from their ever-increasing volumes of data, the lakehouse architecture is a compelling option for data storage and analysis. 

A data lakehouse is a storage technology that combines the scalability and flexibility of a data lake with the structure and reliability of a data warehouse. It brings the best of both worlds together to provide a unified platform for storing, processing, and analyzing data.

The rise of cloud computing has further fueled the adoption of lakehouse architectures, with cloud providers offering managed platforms with lakehouse capabilities that drive big data analysis and business intelligence efforts.

In this article, we will unleash the nuances of Data Lake vs Data Lakehouse, list their key features, use cases, and compare the main differences between the two data storage systems. We have also listed the steps for transitioning from a lake to a lakehouse effectively.

Importance of Data Storage in Modern Data Management

In today's dynamic data management realm, robust storage solutions are paramount. As organizations tackle massive data volumes, the need for efficient storage mechanisms becomes crystal clear. Comparing Data Lakes vs Data Lakehouses underscores the pivotal role of storage.

It forms the bedrock of modern data strategies, enabling seamless storage, organization, and access to vast datasets. With the proliferation of digital technologies and the exponential growth of data, organizations are faced with the challenge of managing diverse data types, ranging to choose between structured vs unstructured data vs semi-structured data.

Effective data storage solutions provide scalable, reliable, and accessible infrastructure, crucial for overcoming data management challenges. They also ensure data governance, security, and compliance, addressing regulatory requirements and privacy concerns. Robust storage, featuring encryption, access controls, and data lineage tracking, maintains data integrity and regulatory compliance.

Both Data Lakes and Data Lakehouses depend on efficient storage for processing and analyzing vast data volumes, each with unique architectural approaches. Understanding the pivotal role of data storage empowers organizations to craft agile, scalable, and secure data management strategies in our data-centric era.

Understanding Data Lakes

A data lake is a centralized repository that stores large volumes of structured, semi-structured, and unstructured data. It allows organizations to store and analyze diverse data types in their native formats.

It is designed for batch processing and large-scale data storage of raw data from various sources, like sensors, logs, social media, streaming data, and more. Lakes employ a "schema-on-read" approach. This means that business data is transformed at the time of analysis rather than during the data ingestion process.

Data engineers can customize the schema and organization of the data based on specific use cases or analytical requirements. This flexibility allows for agility in exploring and analyzing the data.

Data lakes serve as a foundation for various processing and analytics tasks. Data within lakes can be transformed and analyzed using tools and techniques, including machine learning and artificial intelligence, to gain insights that can improve business operations. You can also head to checkout an insightful article on Data Lake vs. Data Warehouse.

What are the advantages of Data Lakes?

• ‍Storage of raw, unprocessed data: Lakes store data in its original, unaltered form without enforcing a predefined schema. This enables organizations to transform data to match varying use cases and avoid data loss or filtering during data ingestion.‍
• Scalability and cost-effectiveness: Data lakes are highly scalable. They can accommodate petabytes or even exabytes of data, allowing organizations to scale their storage and processing capabilities as their data needs grow.‍
• Support for various data types: A data lake can handle structured data (e.g., relational databases), semi-structured data (e.g., JSON, XML), and unstructured data (e.g., text, images, videos).

Challenges faced in traditional data lakes

• ‍Data governance issues: Lakes lack centralized data governance. This makes it harder to maintain data consistency and security. They can become messy and unmanageable without proper controls, leading to data integrity issues.‍
• Potential for slower query performance: Since data lakes store many data types, including unstructured data, poor organization can lead to fragmented and siloed lakes or data swamps. This can hinder data discovery and lead to redundant or inconsistent data. All of these factors can reduce query performance.‍
• Lack of concurrent transaction support: Concurrent transaction support refers to the ability to handle multiple simultaneous read and write operations. A data lake does not support concurrent transactions, so when many users try to access or modify data simultaneously, it can lead to data inconsistency or integrity issues.

The Emergence of Lakehouse

To overcome some of the limitations of data lakes, data engineers use a newer big data storage architecture called a data lakehouse.

A Data Lakehouse is a data management architecture that combines the elements of a data lake and a data warehouse.

In lakehouse data storage, raw source data is stored in a data lake. The lakehouse has built-in data warehouse elements, like schema enforcement and indexing, which data teams can use to transform data for analysis, maintain data integrity, and simplify governance.

Organizations can implement data quality processes to ensure data accuracy and reliability, including data profiling, cleansing, validation, and metadata management.

Advantages of a Lakehouse

• ‍Combines the benefits of a data lake and a data warehouse
  A data lakehouse combines the advantages of data lakes and data warehouses to store structured, semi-structured, and unstructured data in a unified data lake. 
  This eliminates the need for separate data silos and enables data teams to perform analytics and derive insights directly on the raw data without data movement or duplication.
  ‍
• Supports transactional consistency and versioning
  The data lakehouse architecture incorporates ACID (Atomicity, Consistency, Isolation, Durability) transaction support and versioning, enabling multiple users or applications to interact with the data concurrently without compromising data integrity and consistency.
  ‍
• Improved query performance
  Unlike lakes, which use a schema-on-read approach, data lakehouses support schema enforcement and evolution. Data can be stored with a predefined schema using a schema-on-write approach, facilitating easier data integration and analysis.

Data Lake vs Lakehouse: Key Differences

Here is a table highlighting the main differences between a data lake and a data lakehouse:

Data organization & Query Performance

In the data lake architecture, data is stored in its raw format. It can handle high volumes of structured, semi-structured, and unstructured data.

Lakehouses enforce a predefined schema and have data transformation capabilities, allowing semi-structured and unstructured data to be standardized before storage. They store large amounts of structured data.

While both data lakes and lakehouses support data analytics, the structured data within a lakehouse enables advanced analytics. Data lakehouses also integrate with data processing engines to enable efficient real-time analytics and interactive querying.

Data lakes traditionally rely on external processing engines or frameworks, such as Apache Spark or Hadoop, for data processing and analysis. This could slow query performance and require data teams to use additional tools in their stack.

Scalability & Cost

Lakehouses and lakes are both highly scalable. Data lakes can be scaled horizontally by adding more storage and processing nodes to handle increased workloads and data volumes.

Data lakehouses leverage cloud-based storage, integration, and processing services that can automatically scale based on demand. The integration of processing engines, like Apache Spark, also allows for scalable data processing.

Both solutions are cost-effective, with most cloud data lake providers and data lakehouse platforms offering pay-as-you-go models and many pricing tiers. So, data teams only pay for the resources they use.

However, a data lakehouse can reduce costs further since it minimizes data movement and duplication via integrated data processing and analytics.

Concurrency & Transaction Support

Traditional data lakes do not have built-in support for concurrent transactions. They are more suitable for batch processing and sequential data access. They are built to store and process large data volumes without enforcing transactional guarantees.

A data lakehouse supports ACID properties, so concurrent operations do not lead to data inconsistency. This allows for real-time analytics, collaborative data processing, and concurrent access.

Data Governance

Data lakes do not have centralized governance and control mechanisms. Governance in data lakes relies heavily on data stewardship and collaboration between data owners, stewards, and users to ensure quality and compliance.

However, governance practices in data lakes are also more flexible and adaptable to diverse sources and structures.

By contrast, data lakehouses prioritize governance. They use schema enforcement for better control over data structure and consistency. 

Schema enforcement also makes it easier to maintain data standards and ensure compliance during ingestion.

Use Cases & Examples

When to choose a Data Lake?

A data lake is a suitable choice in various scenarios. These include:

• Handling diverse, large-scale data: A data lake is the best choice if your organization deals with various sources and formats. It provides a flexible storage environment to ingest and store raw data without upfront data modeling or schema definition

• Exploration and discovery: Data lakes are well-suited for exploratory analysis and discovery. Since data is stored in its native format, data scientists and analysts can perform ad-hoc queries, data exploration, and data mining to gain insights and discover patterns.

• Agility and flexibility: Data lakes provide data ingestion and processing agility. Additionally, the schema-on-read approach enables users to define and apply schemas during data access, so they can quickly adapt to evolving analysis needs.

• Data science and machine learning: By storing raw data and leveraging scalable processing frameworks, data scientists can perform complex data transformations, feature engineering, and model training directly on the data lake.

The raw and comprehensive nature of the data can also enhance the effectiveness of machine learning algorithms and data-driven models.

• Archiving and data retention: Data lakes can be a cost-effective solution for long-term data archiving and retention. Organizations can store historical data for future analysis or compliance purposes by leveraging a data lake's scalability.

When to consider transitioning to a data lakehouse?

Transitioning to a lakehouse architecture can be beneficial in several scenarios:

• Robust governance: If your organization requires strong governance, data quality control, and data consistency, a lakehouse can provide the necessary organization and management features.

• Concurrent data processing: A lakehouse architecture is advantageous if your organization needs to support simultaneous data processing.

• Real-time analytics: A data lakehouse is a good choice if your organization requires real-time or near-real-time analytical capabilities. Integrated data processing engines facilitate faster insights and querying on the freshest data.

• Simplified data integration: If your organization faces challenges in integrating data from diverse sources, a lakehouse architecture can streamline the process. By enforcing a consistent structure, you can simplify data integration efforts, ensure data compatibility across different systems, and enable more efficient SQL queries.

• Enhanced data analysis: If your organization needs advanced data analytics beyond basic data exploration, a data lakehouse is the way to go. They can support complex analytics, machine learning, and data-driven decision-making.

Real-world examples of companies leveraging a lakehouse architecture

Here are some real-world examples of companies using data lakehouses to help you better understand how you can implement them:

• Airbnb: Airbnb upgraded to a lakehouse architecture to enhance its data infrastructure. Their new stack, based on Apache Iceberg and Spark 3, helped them save more than 50% on compute resources and 40% on job elapsed time reduction in their data ingestion framework.

• Netflix: Netflix uses a lakehouse architecture to handle massive amounts of data and enable data-driven decision-making. With a vast content library and a large user base, a lakehouse enables Netflix to store and process data from diverse sources, perform data analytics, personalize content recommendations, and optimize the user experience.

• Atlassian: Atlassian embraced a lakehouse architecture to enable data democratization at scale. By transitioning to a lakehouse, Atlassian reduced storage, computing, and overhead costs, improved governance, and provided autonomy for their data engineers and scientists to explore and perform analyses that drive innovation.

How to Transition from a Data Lake to a Lakehouse?

Here are 10 critical steps that form a general roadmap to guide you through the transition process:

• Assess current data lake: Evaluate your existing data lake infrastructure, sources, and data management practices. Understand the strengths, limitations, and pain points of your current architecture and identify areas for improvement.
• Define objectives and requirements: Clearly define the specific benefits you aim to achieve by moving to a lakehouse and the use cases that they are built for. This includes improved governance, transactional capabilities, real-time analytics, efficient machine learning, and enhanced data quality. 
• Design data schemas: Determine the schemas required for your structured data, including data types, relationships, and constraints. Plan the schema design based on your analysis needs and integration requirements.
• Implement schema enforcement: Implement mechanisms to enforce schema on the structured data in your data lake. You can use tools like Iceberg or Delta Lake to evolve your data lake into a lakehouse by adding schema enforcement and support for concurrent transactions.
• Data transformation and migration: Transform and migrate your existing data in the data lake to conform to the new schema. Ensure data quality and consistency during migration, and validate the transformed data against the new schema.
• Integrate transactional processing: Integrate transactional processing capabilities into your lakehouse architecture by leveraging tools.
• Implement governance practices: Define data ownership, data stewardship roles, and responsibilities for the lakehouse. Establish data policies, data quality metrics, and data access controls.
• Evolve analytics and applications: Adapt your data analytics tools and applications to leverage the benefits of the data lakehouse architecture. Modify your tools and processes to interact with structured data and capitalize on the enhanced governance features.
• Training and adoption: Educate users on the benefits and capabilities of the lakehouse to foster a culture of data-driven decision-making and collaboration within the lakehouse environment.
• Iterative improvement: Continuously monitor and refine your data lakehouse architecture based on feedback and evolving requirements. Collect insights from data usage, performance metrics, and user feedback to identify areas for optimization.

Transitioning from a data lake to a lakehouse requires careful planning, collaboration, and adopting new tools and practices. 

The process may involve a phased approach, starting with a pilot project or specific use cases before scaling to the entire data lake.

Evaluate your organization's needs and objectives carefully to tailor the transition process effectively. You can also conduct a cloud migration assessment by auditing current data infrastructure, sources, volumes, and network capacity while documenting cloud platform requirements.

Top 4 Tools & Platforms that support Lakehouse Architecture

There are many tools and platforms to support the implementation of a lakehouse. Some popular options include:

• Apache Iceberg: Apache Iceberg is an open-source table format for huge analytic tables. It supports flexible SQL commands, schema evolution, and more for data lakes. Data teams can use multiple engines to operate on the same dataset in a transactionally consistent manner.
• Delta Lake: Delta Lake is an open-source storage layer that runs on top of existing data lakes, providing support for ACID transactions, data versioning, and schema enforcement. It offers reliability, data integrity, and improved performance for analytics and data processing workloads.
• Databricks: Databricks is a data lakehouse platform for data warehousing, analytics, and AI use cases. It allows you to create scalable data pipelines, perform real-time analytics, and leverage transactional capabilities on data stored in a data lake.
• Snowflake: Snowflake is a cloud-based data platform that uses a lakehouse approach. It provides a single platform for data storage and analytics, combining a data lake's scalability with a data warehouse's performance and concurrency.

Explore our detailed article comparing Databricks vs Snowflake to gain deeper insights into their features, functionalities, and suitability for lakehouse architecture.

Conclusion

After thoroughly analyzing the debate of Data Lake vs Data Lakehouse architectures and their impact on data strategy, it's evident that they differ significantly in data structures, processing methods, transactional capabilities, governance frameworks, and analytical functionalities.

While Data Lakes rely on batch processing and necessitate additional steps for data transformation and integration before analysis, Data Lakehouses leverage data warehousing features to facilitate real-time or near-real-time data processing, enabling instant analytics and interactive querying directly on structured data.

By integrating schema enforcement and transactional support, Data Lakehouses offer a more robust and controlled environment for data management and analytics. Choosing between a Data Lake and transitioning to a Lakehouse architecture hinges on your organization's specific requirements, data characteristics, and analytical prerequisites. Assessing these factors and weighing the associated trade-offs is essential for making a well-informed decision.

You can learn more about data storage systems, data analytics, and emerging trends by reading our blog!

FAQs

What is the benefit of data lakehouse over data lake and data warehouse?

A data lakehouse combines the scalability and flexibility of the data lake with the data management and performance characteristics of the data warehouse. A unified architecture allows for processing data in real-time, conducting advanced analytics, and implementing easier data governance within one platform.

What is the difference between a data hub and a data lake?

A data hub is a central location that standardizes data, allowing fast and efficient data sharing among systems. A data lake is similar to a storage system where data is kept in raw form and native format. It provides flexibility for analytics but has none of the inherent management features of a data hub.

Is Snowflake a data lake or warehouse?

While Snowflake is primarily a cloud data warehouse, it provides several key features that bring it closer to the definition of a data lake, such as processing semistructured data and providing native support for scalable storage and computing.

What is an example of a data lakehouse?

Databricks is one of the very common data lakehouses where 'data lakes' and 'data warehouses' converge into one whole to serve many data workloads, from big data analytics to machine learning.

What is the difference between a data lakehouse and a delta lake?

While a data lakehouse is an overarching architecture that brings together the concepts of a data lake and a data warehouse, a Delta Lake is simply a storage layer on top of a data lake for providing ACID transactions, schema enforcement, and so on, making it an essential constituent of a lakehouse.

How do you prepare and load data into a lakehouse?

Data from various sources is ingested into a raw zone, staged for cleaning and transformation into a staging zone, and then loaded into the curated zone for analysis. The standard technologies used today during this process include Apache Spark, Delta Lake, and ETL pipelines.